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Online Repetitive Transcranial Magnetic Stimulation of Dorsomedial and Dorsolateral Prefrontal Cortex in Cognition Decision Making, and Cognitive Dissonance
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The cognitive mechanisms behind wishful predictions: A diffusion model decomposition.

Jeremy D Strueder1, Inkyung Park1, J Toby Mordkoff1

  • 1Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA.

Cognition
|December 4, 2025
PubMed
Summary

Wishful thinking, or desirability bias, inflates expectations by reducing the evidence needed for desired outcomes. This cognitive bias also influences how people interpret evidence, demonstrating a top-down effect on motivated reasoning.

Keywords:
Desirability biasDrift-diffusion modelingMotivated reasoningPredictionsUncertaintyWishful thinking

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Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Behavioral Economics

Background:

  • Wishful thinking, or desirability bias, occurs when the desire for an outcome increases the expectation of its occurrence.
  • Previous research shows outcome desirability influences predictions, but cognitive mechanisms remain unclear.
  • Potential mechanisms include biased evidence judgment or biased evidence search and accumulation.

Purpose of the Study:

  • To investigate the cognitive mechanisms underlying desirability bias using drift-diffusion modeling.
  • To determine at which processing levels desirability exerts its influence on predictions.
  • To examine how desirability affects evidence judgment and accumulation.

Main Methods:

  • Participants (N=147) predicted the color of a randomly selected square from 2-color grids.
  • Outcome desirability was manipulated, with certain colors being more desirable than others.
  • Evidence strength was varied by altering the proportion of desired-color squares; drift-diffusion modeling was employed.

Main Results:

  • Both desirability manipulations and their interaction significantly affected predictions.
  • Drift-diffusion model analysis revealed a judgment-level bias: less evidence was required to predict a desired outcome.
  • Desirability influenced evidence accumulation, with participants more readily interpreting evidence as supporting desired outcomes (top-down influence).

Conclusions:

  • Desirability bias impacts both the threshold for evidence judgment and the process of evidence accumulation.
  • This suggests a top-down influence of desire on how prediction-relevant information is processed.
  • Drift-diffusion modeling is a valuable tool for dissecting the mechanisms of motivated cognitive biases.